class_docstr: The dynamic component that allows user to add controlled variables,
  providing one building block for the dynamic linear model.
  It decribes a dynamic component in the time series data. It basically allows
  user to supply covariate or controlled variable into the dlm,
  and the coefficients of the features will be trained as the latent states.
  Examples are holiday indicators, other observed variables and so on.
  
  Args:
      features: the feature matrix of the dynamic component
      discount: the discount factor
      name: the name of the dynamic component
      w: the value to set the prior covariance. Default to a diagonal
         matrix with 1e7 on the diagonal.
  
  Examples:
      >>>  # create a dynamic component:
      >>> features = [[1.0, 2.0] for i in range(10)]
      >>> ctrend = dynamic(features = features, name = 'random', discount = 0.99)
      >>>  # change the ctrend to have covariance with diagonals are 2 and state 1
      >>> ctrend.createCovPrior(cov = 2)
      >>> ctrend.createMeanPrior(mean = 1)
  
  Attributes:
      d: the dimension of the features (number of latent states)
      n: the number of observation
      componentType: the type of the component, in this case, 'dynamic'
      name: the name of the trend component, to be supplied by user
            used in modeling and result extraction
      discount: the discount factor for this component. Details please refer
                to the @kalmanFilter
      evaluation: the evaluation matrix for this component
      transition: the transition matrix for this component
      covPrior: the prior guess of the covariance matrix of the latent states
      meanPrior: the prior guess of the latent states
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docstr: For deleting the feature data of a specific date.
  
  Args:
      date: the index of which to be deleted.
docstr_tok: ["For","deleting","the","feature","data","of","a","specific","date",".","Args",":","date",":","the","index","of","which","to","be","deleted","."]
code: def popout(self, date):
      """For deleting the feature data of a specific date.
  
      Args:
          date: the index of which to be deleted.
  
      """
      self.features.pop(date)
      self.n -= 1
  
file_path: pydlm/pydlm/modeler/dynamic.py
imports: import numpy
  from collections.abc import MutableSequence
  from copy import deepcopy
  import pydlm.base.tools
  from .component import component
label_desc: Use image node_id 10 for calling the dynamic obj's underlying member method code with example usage: obj.popout(date) without return types